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Information Contagion through Social Media: Towards a Realistic Model of the Australian Twittersphere


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Paper by Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, Felix Münch, Lucia Falzon, and Lucy Resnyansky presented at the ACSPRI 2016 conference, Sydney, 19-22 July 2016/

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Information Contagion through Social Media: Towards a Realistic Model of the Australian Twittersphere

  1. 1. UNCLASSIFIED – Approved For Public release Information Contagion through Social Media: Towards a Realistic Model of the Australian Twittersphere Work in progress Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, and Felix Münch Digital Media Research Centre Queensland University of Technology Lucia Falzon and Lucy Resnyansky NSID DSTG 5th Biennial ACSPRI Social Science Methodology Conference July 19-22, 2016, The University of Sydney
  2. 2. Introduction  AREA: the development of contagion simulation approaches  AIM: to simulate the effects of a range of possible communication strategies on a network structure that accurately replicates the real-world Twitter follower network in Australia  FOCUS: crisis communication during two qualitatively different events: – the Brisbane flood: impacted on a large geographical area and on a large number of people, either as an actual or a potential threat; – The Sydney siege: located at a single point, and directly impacted only on a small number of people, but was the focus of attention for many who were located at a significant distance from the actual event location  OUTCOMES: – new methodological impulses for the modelling of realistic information contagion processes in social media – directly actionable insights into the specific processes of information contagion in crisis contexts within the Australian Twittersphere.
  3. 3. Fabrega, J., & Paredes, P. (2013). Social Contagion and Cascade Behaviors on Twitter. Information, 4(2), 171-181. [Twitter] Hodas, N. O., & Lerman, K. (2014). The simple rules of social contagion. Scientific reports, 4. [Twitter] Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS 111(24): 8788–90. [Facebook] Shuai, X., Ding, Y., Busemeyer, J., Chen, S., Sun, Y., & Tang, J. (2012). Modeling indirect influence on twitter. International Journal on Semantic Web and Information Systems (IJSWIS), 8(4), 20-36. [Twitter] Weng, L., Menczer, F., & Ahn, Y.-Y. (2013). Virality Prediction and Community Structure in Social Networks. Sci. Rep., 3. doi: 10.1038/srep0252 [Twitter] Social Contagion in Social Media: Literature
  4. 4. Important Concepts and Definitions for This Project 1. Contagion as a property of a spreading object: the inherent virality 2. Contagion as a process: simple vs. complex contagion 3. Contagion as a structural property: virality of a cascade
  5. 5. Virality of a Cascade (Goel et al. 2013)
  6. 6. The Australian Twittersphere  Twitter in Australia: – Strong take-up since 2009 – Centred around 25-55 age range, urban, educated, affluent users (but gradually broadening) – Significant role in crisis communication, political communication, audience engagement, …  Mapping the Twittersphere: – Long-term project to identify all Australian Twitter accounts – First iteration: snowball crawl of follower/followee networks • Starting with key hashtag populations (#auspol, #spill, …) • Map of ~1m accounts in early 2012 – Second iteration: full crawl of global Twitter ID numberspace through to Sep. 2013 (~870m accounts) • Filtering by description, location, timezone fields • Focus on identifiably Australian cities, states, timezones and other markers • 2.8 million Australian accounts identified (by Sep. 2013) • Retrieval of their follower/followee lists • Best guess of account location based on timezone, location and description settings
  7. 7. Education Agriculture Literature Adelaide / SA Food Wine Beer Parenting Mums PR Netizens Marketing Investing Real Estate Home Business Sole Traders Self-Help HR / Support Followback Urban Media Utilities Advertising Business Fashion Beauty Arts Cinema Journalists Politics Hard RightLeftists News CyclingTalkback Music TV V8s UFC NRL AFL Football Horse Racing Cricket NRU Celebrities Hillsong Perth Pop Media Teen Idols Cody Simpson The Australian Twittersphere 2.8m known Australian accounts Network of follower connections Filtered for degree ≥1000 140k nodes (~5%), 22.8m edges Labels assigned through qualitative evaluation
  8. 8. TrISMA: Tracking Australian Twitter  ARC LIEF project: – Tracking Infrastructure for Social Media Analysis – Multi-university project led by QUT to develop comprehensive infrastructure for large-scale social media data analytics – Twitter: continuous capture of tweets by all 2.8m identified Australian accounts – 1b+ tweets captured to date, 1m+ new tweets/day – Data storage via Google BigQuery, analysis via Tableau and Gephi
  9. 9. Modelling Experiments Two issues to be addressed: 1. The impact that accounts with certain characteristics during the early phases of the crisis communication process have on the overall dissemination of emergency messages. 2. The impact that using Twitter-specific communicative features – e.g. a topical hashtag – have on the dissemination of emergency messages.
  10. 10. Research Assumptions & Questions ASSUMPTION (1): Contagion behaviour on Twitter is affected by a range of factors that are specific to individual users and local subsets of the wider network. Modelling of contagion processes based on realistic data can identify the impact of these factors. QUESTION (1): What is the impact of factors such as the following on contagion processes: volume of incoming content feeds to an individual account; repeated exposure to contagious content; network position of immediately preceding vector of contagion; affinity of message content to recipient's key interests; ...?
  11. 11. Research Assumptions & Questions ASSUMPTION (2): People are more likely to share information with those who are similar to them (Romero et al. 2010). QUESTION (2): Does this assumption still hold in times of crisis? In the case of the Australian Twittersphere, does the community structure (clusters of users based on similarity of interests, tastes, demographics, etc.) change to reflect the fact that in crises the basis of online community structuring differs from normal life situations?
  12. 12. Research Assumptions & Questions ASSUMPTION (3): Rumour diffusion on social media can be modelled in the same way as the spread of rumours according to a traditional model based on studies of rumours in a mass media society. The traditional rumour model (Andrews et al. 2016; Freberg 2012): • an official source determines the certainty and veracity of a piece of information • information is a rumour only until it is either confirmed or denied by the official source • rumours only spread until certainty is established. QUESTION (3): How significant is the impact of official tweets on rumour spreading? What implications does this have for the communication strategies of official stakeholders?
  13. 13. Factors to Consider to In Understanding Retweeting Patterns  The relevance of the information source to the crisis situation has a bigger impact on retweetability than the large number of subscribers or followers  The waiting time of a retweet in the context of extreme events: hashtags are strongly correlated with shorter waiting times whereas a large number of followers is associated with longer waiting times; messages broadcast to a large number of individuals may make re-tweeting redundant. (Spiro et al. 2012); Case study: the Sky News Australia Lakemba Raids rumour vs. the AFPMedia tweet denying the rumour (Arif et al. 2016) - due to a lack of serial transmission the Sky News tweet had a very low impact on the rumour’s overall propagation. Even though the AFP had much fewer followers their message could spread to a larger network - a large number of followers may be counter-productive for rumour spreading because people might assume that others have already seen the message.
  14. 14. Possible Modelling Parameters  Snowball sampling from seeds within each cluster, with the number of seeds in proportion to the size of cluster.  Modelling at the network cluster level instead of the current node level modeling. Currently model using network wide metrics for generation, we want to expand this to allow for different coefficients for different clusters in the network.  Incorporate different behaviour for different types of nodes, for example individuals, media outlets, bots of different types, verified users.  Introduce additional metrics such as tweeting rate (number of tweets sent during period) and proportion of tweets, retweets, @mentions and hashtag use for each node  Extract these new metrics and the topic preferences from the selected nodes in the Australian Twittersphere.  Model effect of geographic location in addition to network location.
  15. 15. Key Measures in Modelling  Extent of serial transmission (time-ordered reach)  Overall propagation (proportion of nodes a message reaches, this measure is related to information exposure the total number of people who have been exposed to messages related to a particular piece of information)  Retweet waiting time – the time between the first posting of a message and its re-transmission
  16. 16. Simulation model  An agent-based model has been developed in order to simulate information contagion in a social media network.  The agents in the network are “accounts” that are follow other accounts and create “posts” that diffuse through the network based on certain rules.  There are two main processes that are modelled; network generation and information diffusion (outlined below).  The model is very much a work in progress at this stage and will be developed as the project continues.
  17. 17. Account Characteristics 0 20 40 60 80 100 Topic A Topic B Topic CTopic D Topic E Account 1 Account 2  The accounts are modelled as having a specific interest profile.  The profile consist of nt topics t, and each account’s interest it in a specific topic varies between 0 and 100 (see illustration).  Account are more likely to create and pass on posts that fit their interest profile.  Accounts are also likely to follow other accounts with a similar interest profile (homophily). Example:
  18. 18. Brief Overview of the Network Generation Process in the Simulation Model  The model can either load a real-world network structure or generate an artificial network that replicates characteristics of the real-world social network.  When an artificial network is generated it is controlled by the following weighted parameters: – The extent a new account is likely to connect with accounts… • …beyond the friends of its current friends. (Introversion) • …with a similar profile (Homophily) • …that already have many followers (Popularity preference) • …that are active communicators (Communicator preference) – The likelihood that an existing account starts following another account, based on the parameters listed above.
  19. 19. Brief Overview of the Network Generation Process in the Simulation Model  Posting – The probability that an account sends a post during a timestep is p (exp distr). – When an account creates a post, the post is assigned to one of the topics in the account’s interest profile.  Observing – An account receives posts from the accounts that it follows. – An account keeps the x most recently received posts in its newsfeed.  Re-posting – The likelihood that an account re-posts a post in their newsfeed is p, which is a function of how well the post fits with the account’s interest profile. – A post is only re-posted once.
  20. 20. The Simulation Model Interface
  21. 21. Example of a Basic Simulation Experiment Output data is recorded to allow for post-simulation analysis. This is an example of a basic simulation experiment that explores the relationships between the size of the creator’s 1-step and 2-step neighbourhoods and the number of accounts that (a) see the post and (b) re-tweet the post. As expected, the experiment shows that there is a stronger correlation between the size of the 2-step neighbourhood on the diffusion of the posts.
  22. 22. References  Andrews, C., Fichet, E., Ding, Y. Spiro, E.S., Starbird, K., 2016, ‘Keeping up with the Tweet-dashians: The impact of ‘official accounts on online rumoring’, CSCW’16, February 27 – March 2, 2016, San Francisco, CA, USA, ACM, pp. 452-465.  Bruns, A., & Burgess, J. (2015). Twitter Hashtags from Ad Hoc to Calculated Publics. In N. Rambukkana (Ed.), Hashtag Publics: The Power and Politics of Discursive Networks (pp. 13–28). New York: Peter Lang.  Bruns, A., Burgess, J., & Highfield, T. (2014). A “Big Data” Approach to Mapping the Australian Twittersphere. In P. L. Arthur & K. Bode (Eds.), Advancing Digital Humanities: Research, Methods, Theories (pp. 113–129). Houndmills: Palgrave Macmillan.  Bruns, A., J. Burgess, J. Banks, D. Tjondronegoro, A. Dreiling, J. Hartley, T. Leaver, A. Aly, T. Highfield, R. Wilken, E. Rennie, D. Lusher, M. Allen, D. Marshall, K. Demetrious, & T. Sadkowsky. (2015). TrISMA: Tracking Infrastructure for Social Media Analysis.  Romero, D., Meeder, B., Kleinberg, J. (2011) Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter, Proceedings of WWW’11 – the 20th international conference on World Wide Web, pp695-704 Accessed 31/3/13 at  Spiro, E.S., DuBois, C.L., Butts, C.T., ‘Waiting for a Retwwet: Modeling Waiting Times in Information Propagagtion’, Workshop on Social Network and Social Media Analysis: Methods, Models and Applications. Neural Information Processing Systems (NIPS), January 2012, Lake Tahoe, NV. Accessed 14/3/15 at